Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7facf42e92e8>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7facf109f4a8>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.1
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    input_images = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels),
                                  name="input_real")
    z_data = tf.placeholder(tf.float32, (None, z_dim), name="input_z")
    lr = tf.placeholder(tf.float32, name="lr")

    return input_images, z_data, lr


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the generator, tensor logits of the generator).

In [6]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param image: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    alpha = 0.2
    with tf.variable_scope('discriminator', reuse=reuse):
        # Input layer is 32x32x3
        x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same', 
                              kernel_initializer=tf.contrib.layers.xavier_initializer_conv2d(uniform=False))
        relu1 = tf.maximum(alpha * x1, x1)
        # 
        
        x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same',
                             kernel_initializer=tf.contrib.layers.xavier_initializer_conv2d(uniform=False))
        # bn2 = x2
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = tf.maximum(alpha * bn2, bn2)
        
        x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same',
                             kernel_initializer=tf.contrib.layers.xavier_initializer_conv2d(uniform=False))
        # bn3 = x3
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = tf.maximum(alpha * bn3, bn3)
        
        shape = relu3.get_shape().as_list()
     
        # Flatten it
        flat = tf.reshape(relu3, (-1, 4 * 4 * 256))
        logits = tf.layers.dense(flat, 1, kernel_initializer=tf.contrib.layers.xavier_initializer(uniform=False))
        out = tf.sigmoid(logits)
        
        return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    alpha = 0.2
    training = is_train
    output_dim = out_channel_dim
    reuse = False if is_train else True
    with tf.variable_scope('generator', reuse=reuse):
        # First fully connected layer
        x1 = tf.layers.dense(z, 2*2*512, activation=None, use_bias=False,
                             kernel_initializer=tf.contrib.layers.xavier_initializer(uniform=False))
        # Reshape it to start the convolutional stack
        x1 = tf.reshape(x1, (-1, 2, 2, 512))
        x1 = tf.layers.batch_normalization(x1, training=training)
        x1 = tf.maximum(alpha * x1, x1)
        # 2x2x512 now
        
        x2 = tf.layers.conv2d_transpose(x1, 256, 5, strides=2, padding='valid', use_bias=False,
                                       kernel_initializer=tf.contrib.layers.xavier_initializer_conv2d(uniform=False))
        x2 = tf.layers.batch_normalization(x2, training=training)
        x2 = tf.maximum(alpha * x2, x2)
        # 8x8x256 now
        
        x3 = tf.layers.conv2d_transpose(x2, 128, 5, strides=2, padding='same', use_bias=False,
                                       kernel_initializer=tf.contrib.layers.xavier_initializer_conv2d(uniform=False))
        x3 = tf.layers.batch_normalization(x3, training=training)
        x3 = tf.maximum(alpha * x3, x3)
        # 16x16x128 now
        
        # Output layer
        logits = tf.layers.conv2d_transpose(x3, output_dim, 5, strides=2, padding='same',
                                           kernel_initializer=tf.contrib.layers.xavier_initializer_conv2d(uniform=False))
        # 32x32x3 now
        
        out = tf.tanh(logits)
        
        return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    output_dim = out_channel_dim
    g_model = generator(input_z, output_dim, is_train=True)
    d_model_real, d_logits_real = discriminator(input_real, reuse=False)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)

    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_logits_real) * 0.9))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_logits_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_logits_fake)))

    d_loss = d_loss_real + d_loss_fake

    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()
In [ ]:
 

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [11]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    image_channels = 3 if data_image_mode == "RGB" else 1
    input_real, input_z, lr = model_inputs(data_shape[1], data_shape[2], image_channels, z_dim)
    d_loss, g_loss = model_loss(input_real, input_z, image_channels)
    d_train_opt, g_train_opt = model_opt(d_loss, g_loss, lr, beta1)
    
    
        
    steps = 0
    samples, losses = [], []
    sample_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))


    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        sess.run(tf.local_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                steps += 1
                batch_images = batch_images * 2

                # TODO: Train Model
                
                
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))

                # Run optimizers
                _ = sess.run(d_train_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr : learning_rate})
                _ = sess.run(g_train_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr : learning_rate})
                
                if steps % 100 == 0:
                    # At the end of each epoch, get the losses and print them out
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i + 1, epochs),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                    # Save losses to view after training
                    losses.append((train_loss_d, train_loss_g))

                if steps % 100 == 0:
                    show_generator_output(sess, 25, input_z, image_channels, data_image_mode)
                
                
                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [14]:
batch_size = 128
z_dim = 100
learning_rate = 0.001
beta1 = 0.5
alpha = 0.2

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 5

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/5... Discriminator Loss: 0.4298... Generator Loss: 7.1793
Epoch 1/5... Discriminator Loss: 0.6129... Generator Loss: 1.9274
Epoch 1/5... Discriminator Loss: 0.5427... Generator Loss: 2.9321
Epoch 1/5... Discriminator Loss: 0.8762... Generator Loss: 1.6678
Epoch 2/5... Discriminator Loss: 0.9987... Generator Loss: 1.1326
Epoch 2/5... Discriminator Loss: 0.8900... Generator Loss: 1.2459
Epoch 2/5... Discriminator Loss: 0.9344... Generator Loss: 1.2556
Epoch 2/5... Discriminator Loss: 1.0922... Generator Loss: 2.2150
Epoch 2/5... Discriminator Loss: 1.3885... Generator Loss: 2.7762
Epoch 3/5... Discriminator Loss: 0.9386... Generator Loss: 1.0812
Epoch 3/5... Discriminator Loss: 0.7616... Generator Loss: 1.7505
Epoch 3/5... Discriminator Loss: 0.8448... Generator Loss: 1.4333
Epoch 3/5... Discriminator Loss: 1.0500... Generator Loss: 1.0047
Epoch 3/5... Discriminator Loss: 1.7832... Generator Loss: 0.3866
Epoch 4/5... Discriminator Loss: 1.0009... Generator Loss: 1.9942
Epoch 4/5... Discriminator Loss: 1.2291... Generator Loss: 0.7530
Epoch 4/5... Discriminator Loss: 1.8555... Generator Loss: 0.3300
Epoch 4/5... Discriminator Loss: 1.0444... Generator Loss: 1.1546
Epoch 5/5... Discriminator Loss: 0.9861... Generator Loss: 1.1211
Epoch 5/5... Discriminator Loss: 1.7276... Generator Loss: 3.8715
Epoch 5/5... Discriminator Loss: 0.9912... Generator Loss: 0.9488
Epoch 5/5... Discriminator Loss: 0.7855... Generator Loss: 1.2633
Epoch 5/5... Discriminator Loss: 1.0619... Generator Loss: 0.8396
In [ ]:
 

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [15]:
batch_size = 256
z_dim = 100
learning_rate = 0.0001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 25
celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/25... Discriminator Loss: 0.5102... Generator Loss: 2.6968
Epoch 1/25... Discriminator Loss: 0.6686... Generator Loss: 1.7481
Epoch 1/25... Discriminator Loss: 0.5724... Generator Loss: 2.2990
Epoch 1/25... Discriminator Loss: 0.8719... Generator Loss: 1.9434
Epoch 1/25... Discriminator Loss: 0.8132... Generator Loss: 1.7274
Epoch 1/25... Discriminator Loss: 1.1447... Generator Loss: 1.1059
Epoch 1/25... Discriminator Loss: 0.8563... Generator Loss: 1.6287
Epoch 2/25... Discriminator Loss: 0.9228... Generator Loss: 1.6482
Epoch 2/25... Discriminator Loss: 0.9000... Generator Loss: 1.5442
Epoch 2/25... Discriminator Loss: 1.1602... Generator Loss: 1.7177
Epoch 2/25... Discriminator Loss: 0.9419... Generator Loss: 1.5007
Epoch 2/25... Discriminator Loss: 0.9587... Generator Loss: 1.0644
Epoch 2/25... Discriminator Loss: 1.0765... Generator Loss: 1.2305
Epoch 2/25... Discriminator Loss: 0.9511... Generator Loss: 1.3147
Epoch 2/25... Discriminator Loss: 1.2902... Generator Loss: 0.6672
Epoch 3/25... Discriminator Loss: 1.0593... Generator Loss: 1.2706
Epoch 3/25... Discriminator Loss: 0.9777... Generator Loss: 1.5279
Epoch 3/25... Discriminator Loss: 0.8821... Generator Loss: 1.4998
Epoch 3/25... Discriminator Loss: 1.3157... Generator Loss: 2.2056
Epoch 3/25... Discriminator Loss: 1.2710... Generator Loss: 0.6800
Epoch 3/25... Discriminator Loss: 1.0485... Generator Loss: 1.1034
Epoch 3/25... Discriminator Loss: 1.0776... Generator Loss: 1.2567
Epoch 3/25... Discriminator Loss: 0.8813... Generator Loss: 1.5623
Epoch 4/25... Discriminator Loss: 1.1022... Generator Loss: 0.8639
Epoch 4/25... Discriminator Loss: 1.0133... Generator Loss: 1.4924
Epoch 4/25... Discriminator Loss: 1.1652... Generator Loss: 0.8064
Epoch 4/25... Discriminator Loss: 1.0916... Generator Loss: 0.8693
Epoch 4/25... Discriminator Loss: 0.9896... Generator Loss: 1.5657
Epoch 4/25... Discriminator Loss: 1.0298... Generator Loss: 1.0391
Epoch 4/25... Discriminator Loss: 0.9597... Generator Loss: 1.3100
Epoch 4/25... Discriminator Loss: 1.0603... Generator Loss: 1.1212
Epoch 5/25... Discriminator Loss: 1.0173... Generator Loss: 1.1989
Epoch 5/25... Discriminator Loss: 0.9485... Generator Loss: 0.9968
Epoch 5/25... Discriminator Loss: 0.9451... Generator Loss: 1.0916
Epoch 5/25... Discriminator Loss: 1.0485... Generator Loss: 1.0183
Epoch 5/25... Discriminator Loss: 0.9659... Generator Loss: 1.3139
Epoch 5/25... Discriminator Loss: 1.0380... Generator Loss: 1.0227
Epoch 5/25... Discriminator Loss: 1.0094... Generator Loss: 1.6861
Epoch 5/25... Discriminator Loss: 0.9215... Generator Loss: 1.2743
Epoch 6/25... Discriminator Loss: 0.8983... Generator Loss: 1.0230
Epoch 6/25... Discriminator Loss: 0.9611... Generator Loss: 1.1550
Epoch 6/25... Discriminator Loss: 0.9828... Generator Loss: 1.2750
Epoch 6/25... Discriminator Loss: 0.9746... Generator Loss: 1.0229
Epoch 6/25... Discriminator Loss: 0.9760... Generator Loss: 1.1404
Epoch 6/25... Discriminator Loss: 1.0988... Generator Loss: 1.5144
Epoch 6/25... Discriminator Loss: 0.9559... Generator Loss: 1.3685
Epoch 6/25... Discriminator Loss: 1.0134... Generator Loss: 1.7706
Epoch 7/25... Discriminator Loss: 1.1047... Generator Loss: 0.7958
Epoch 7/25... Discriminator Loss: 1.0280... Generator Loss: 1.0646
Epoch 7/25... Discriminator Loss: 1.1673... Generator Loss: 1.9267
Epoch 7/25... Discriminator Loss: 1.1021... Generator Loss: 0.8391
Epoch 7/25... Discriminator Loss: 1.1094... Generator Loss: 0.8244
Epoch 7/25... Discriminator Loss: 0.9068... Generator Loss: 1.3673
Epoch 7/25... Discriminator Loss: 1.0709... Generator Loss: 0.8782
Epoch 7/25... Discriminator Loss: 0.8184... Generator Loss: 2.1078
Epoch 8/25... Discriminator Loss: 1.0814... Generator Loss: 1.2382
Epoch 8/25... Discriminator Loss: 0.9894... Generator Loss: 0.9962
Epoch 8/25... Discriminator Loss: 0.9509... Generator Loss: 1.2706
Epoch 8/25... Discriminator Loss: 1.0090... Generator Loss: 1.8978
Epoch 8/25... Discriminator Loss: 1.1684... Generator Loss: 0.7436
Epoch 8/25... Discriminator Loss: 1.0543... Generator Loss: 1.5349
Epoch 8/25... Discriminator Loss: 0.9151... Generator Loss: 1.3306
Epoch 8/25... Discriminator Loss: 0.9969... Generator Loss: 1.5541
Epoch 9/25... Discriminator Loss: 0.9594... Generator Loss: 0.9887
Epoch 9/25... Discriminator Loss: 1.0842... Generator Loss: 0.9733
Epoch 9/25... Discriminator Loss: 1.0726... Generator Loss: 0.9053
Epoch 9/25... Discriminator Loss: 0.8430... Generator Loss: 1.3464
Epoch 9/25... Discriminator Loss: 1.0605... Generator Loss: 0.9852
Epoch 9/25... Discriminator Loss: 1.0272... Generator Loss: 0.9400
Epoch 9/25... Discriminator Loss: 0.9926... Generator Loss: 1.4335
Epoch 9/25... Discriminator Loss: 1.0558... Generator Loss: 0.8508
Epoch 10/25... Discriminator Loss: 0.8894... Generator Loss: 2.1735
Epoch 10/25... Discriminator Loss: 0.9111... Generator Loss: 1.6013
Epoch 10/25... Discriminator Loss: 0.9793... Generator Loss: 1.0906
Epoch 10/25... Discriminator Loss: 1.3595... Generator Loss: 0.5326
Epoch 10/25... Discriminator Loss: 0.9809... Generator Loss: 1.3754
Epoch 10/25... Discriminator Loss: 1.0688... Generator Loss: 0.9737
Epoch 10/25... Discriminator Loss: 1.0615... Generator Loss: 2.4182
Epoch 10/25... Discriminator Loss: 0.9997... Generator Loss: 0.8855
Epoch 11/25... Discriminator Loss: 0.9206... Generator Loss: 1.6203
Epoch 11/25... Discriminator Loss: 0.9805... Generator Loss: 1.1317
Epoch 11/25... Discriminator Loss: 1.0263... Generator Loss: 0.8624
Epoch 11/25... Discriminator Loss: 1.0696... Generator Loss: 1.8842
Epoch 11/25... Discriminator Loss: 0.9206... Generator Loss: 1.1514
Epoch 11/25... Discriminator Loss: 1.0671... Generator Loss: 0.9627
Epoch 11/25... Discriminator Loss: 1.1722... Generator Loss: 0.7352
Epoch 11/25... Discriminator Loss: 0.9894... Generator Loss: 1.2461
Epoch 12/25... Discriminator Loss: 0.8537... Generator Loss: 1.3074
Epoch 12/25... Discriminator Loss: 1.2779... Generator Loss: 0.6380
Epoch 12/25... Discriminator Loss: 0.9394... Generator Loss: 1.1098
Epoch 12/25... Discriminator Loss: 0.9579... Generator Loss: 1.3495
Epoch 12/25... Discriminator Loss: 0.9786... Generator Loss: 0.9356
Epoch 12/25... Discriminator Loss: 0.8927... Generator Loss: 1.1782
Epoch 12/25... Discriminator Loss: 0.9947... Generator Loss: 1.0081
Epoch 13/25... Discriminator Loss: 1.0220... Generator Loss: 1.2010
Epoch 13/25... Discriminator Loss: 1.0410... Generator Loss: 0.9851
Epoch 13/25... Discriminator Loss: 1.1395... Generator Loss: 1.2194
Epoch 13/25... Discriminator Loss: 0.8881... Generator Loss: 1.1739
Epoch 13/25... Discriminator Loss: 0.9500... Generator Loss: 1.4542
---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-15-88774bfb0942> in <module>()
     12 with tf.Graph().as_default():
     13     train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
---> 14           celeba_dataset.shape, celeba_dataset.image_mode)

<ipython-input-11-546605654c5f> in train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode)
     39                 # Run optimizers
     40                 _ = sess.run(d_train_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr : learning_rate})
---> 41                 _ = sess.run(g_train_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr : learning_rate})
     42 
     43                 if steps % 100 == 0:

/home/michael/apps/miniconda3/envs/py35/lib/python3.6/site-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
    765     try:
    766       result = self._run(None, fetches, feed_dict, options_ptr,
--> 767                          run_metadata_ptr)
    768       if run_metadata:
    769         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

/home/michael/apps/miniconda3/envs/py35/lib/python3.6/site-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
    963     if final_fetches or final_targets:
    964       results = self._do_run(handle, final_targets, final_fetches,
--> 965                              feed_dict_string, options, run_metadata)
    966     else:
    967       results = []

/home/michael/apps/miniconda3/envs/py35/lib/python3.6/site-packages/tensorflow/python/client/session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
   1013     if handle is None:
   1014       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,
-> 1015                            target_list, options, run_metadata)
   1016     else:
   1017       return self._do_call(_prun_fn, self._session, handle, feed_dict,

/home/michael/apps/miniconda3/envs/py35/lib/python3.6/site-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)
   1020   def _do_call(self, fn, *args):
   1021     try:
-> 1022       return fn(*args)
   1023     except errors.OpError as e:
   1024       message = compat.as_text(e.message)

/home/michael/apps/miniconda3/envs/py35/lib/python3.6/site-packages/tensorflow/python/client/session.py in _run_fn(session, feed_dict, fetch_list, target_list, options, run_metadata)
   1002         return tf_session.TF_Run(session, options,
   1003                                  feed_dict, fetch_list, target_list,
-> 1004                                  status, run_metadata)
   1005 
   1006     def _prun_fn(session, handle, feed_dict, fetch_list):

KeyboardInterrupt: 

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.